Global incidence and mortality of childhood leukemia and its relationship with the Human Development Index

Background Childhood leukemia (CL) is a major global concern, accounting for 33% of all new cancer cases and 31% of all cancer deaths in children aged 0–14 years. Our study aimed to analyze the global incidence and mortality rates of CL in 2020 and its relationship with the Human Development Index (HDI). Material and methods In this ecologic study, we analyzed the 2020 cancer incidence and mortality data for children aged 0–14 years from the GLOBOCAN Project. We calculated the Age-Standardized Incidence Rate (ASIR) and Age-Standardized Mortality Rate (ASMR) of CL per 100,000 individuals. Pearson’s correlation coefficient was used to examine the association between childhood leukemia ASIR, ASMR, and the HDI, with a statistical significance threshold of P<0.05. Results In 2020, there were a total of 67,008 new cases of CL worldwide, with males accounting for 57.85%. The global ASIR for CL was 3.4 per 100,000 (3.9 in males, 3 in females). Additionally, there were 25,080 CL-related deaths, with males comprising 58.86%. The overall ASMR for CL was 1.3 (1.4 in males, 1.1 in females). We found a significant positive correlation (r = 0.405, P≤0.001) between the global ASIR and ASMR for CL. There was a strong positive correlation (r = 0.770, P = 0.001) between the HDI and childhood leukemia ASIR, but no significant association (r = 0.077, P = 0.337) was observed with ASMR. Conclusion Our study reveals that CL remains a significant health burden worldwide. We identified a positive correlation between the ASIR of CL and the HDI, indicating a potential role of socioeconomic factors in CL incidence.


Introduction
Childhood leukemia(CL) is a specific type of cancer that primarily affects children and adolescents [1].It is characterized by the abnormal growth and proliferation of immature white blood cells in the bone marrow.This uncontrolled growth hampers the production of healthy blood cells, resulting in a weakened immune system and various symptoms [2].As a blood and bone marrow cancer, CL has a profound impact on the lives of young patients and their families [3,4].Globally, CL is a significant concern, accounting for a significant portion of new cancer cases and cancer-related deaths in children aged 0-14 years.In 2014, it represented approximately 33% of all new cancer cases and 31% of cancer-related deaths within this age group [1,5].
The incidence and mortality rates of CL exhibit significant variations worldwide, with higher rates observed in developed countries [6].While the incidence of CL has been increasing in developed nations, mortality rates have been decreasing [5].However, in low-and middle-income countries, limited access to diagnostic and treatment facilities leads to low survival rates for CL [7,8].
In summary, acute lymphoblastic leukemia (ALL) is the most common subtype of CL, accounting for approximately 75-80% of cases.Acute myeloid leukemia (AML) is less common but still notable, representing around 15-20% of CL cases.Other subtypes such as chronic myeloid leukemia (CML), juvenile myelomonocytic leukemia (JMML), and mixed lineage leukemia (MLL) are rarer and comprise a smaller proportion of CL cases [9].
Leukemia diagnosis requires multiple procedures, such as complete blood count (CBC), coagulation studies, and chemistry profile (specifically assessing liver and kidney function).Analyzing the morphological features of bone marrow aspiration helps distinguish between various types of leukemia [10,11].
The treatment of leukemia is determined by the type and severity of the disease.The standard approaches for childhood leukemia (CL) include multi-agent chemotherapy, immunotherapy, targeted therapy, and bone marrow transplantation.These treatment options are tailored to the specific subtype of leukemia and the individual patient [12,13].Prognosis varies based on factors such as leukemia subtype, age at diagnosis, treatment response, and individual characteristics [14].These treatment methods have significantly reduced the mortality rate of leukemia by over 60% in developed countries, achieving a 5-year disease-free survival rate of over 90% [15][16][17].However, the global incidence of leukemia has been on the rise.Acute lymphoblastic leukemia remains the leading cause of death among children, especially in low-tomiddle-income countries (LMICs)where nearly 94% of leukemia-related deaths occur [18,19].This presents a significant burden in those regions.
Despite significant advancements in medicine, the global projection for new cancer cases in 2025 is expected to exceed 20 million.It is concerning to note that 80% of this burden and 84.1% of childhood cancer cases are projected to occur in LMICs [20].Alarmingly, a projected growth of over 50% by 2030 is expected, driven by factors such as demographic growth, socioeconomic conditions, environmental crises, lifestyle choices, and other biosocial factors [20,21].
Children in LMICs who develop cancer face a substantially higher risk of mortality, with at least four times the vulnerability compared to their counterparts in developed countries [15][16][17].Even neighboring countries within the same continent exhibit disparities in survival rates [15,17,18].Inaccurate diagnosis, limited treatment access, high rates of early relapse, therapyrelated toxicity, lack of public awareness about cancer, and scarce healthcare resources contribute to these low survival rates [11,13,18,22].
The Human Development Index (HDI) is a composite index that ranks countries based on their level of human development, considering health, education, and income indicators.It provides a broader perspective on progress beyond economic factors alone and helps identify strengths and weaknesses in human capital development.Previous studies have examined the association between HDI and cancer incidence and mortality rates [23][24][25][26], including leukemia [27,28].These studies generally found that higher HDI levels are linked to higher cancer incidence and lower mortality rates.Factors such as improved healthcare access, education, socioeconomic status, and healthier lifestyle behaviors contribute to this association.However, further research is needed to understand the specific mechanisms through which HDI influences cancer incidence and mortality.This study aims to analyze the global incidence and mortality rates of CL in 2020 and investigate their correlation with the HDI.By identifying associated factors, it seeks to gain new insights into prevention and management strategies for this disease.

Study design and populations
This study is an ecologic study with analytic elements that focuses on the incidence and mortality rates of leukemia in children aged from birth to 14 years old.Ecologic studies examine the relationship between exposure and outcome at the population level, rather than the individual level.They analyze data for different groups or populations to explore associations between variables.Ecologic studies are valuable for generating hypotheses and identifying patterns on a broader scale, but they cannot establish causality or determine associations at the individual level [29].In this study, we investigate potential correlations between HDI scores and age-standardized CL incidence and mortality rates across different countries.We analyze the data for children aged from birth to 14 years old, as well as separately for boys and girls.Our hypothesis suggests that nations or areas with higher HDI may experience higher incidence rates of CL due to improved diagnosis and reporting.Conversely, countries with lower HDI may have higher mortality rates, indicating limited access to healthcare.The study protocol was approved by the Ethics Committee of Shahrekord University of Medical Sciences (IR. SKUMS.REC.1402.124).

Data sources for incidence and mortality rates of CL
The study used data from the International Agency for Research on Cancer's (IARC) GLOBO-CAN project.GLOBOCAN is a reliable and up-to-date database created by the World Health Organization (WHO).It provides comprehensive information on cancer incidence and mortality rates in 184 countries [30].The database includes data from countries for which information was available in the Global Cancer Observatory database.You can access this database at (https://gco.iarc.fr/today),and the date of access is 25/5/2023.It's important to note that due to data accessibility challenges, information from certain countries was not included in this study.Additionally, in some cases, data from countries with very small populations was not considered [31,32].
In this study, we focused on the Age-Standardized Incidence Rate (ASIR) and Age-Standardized Mortality Rate (ASMR) of CL, and categorized and presented this information based on multiple regional categories.These included continent (Africa, Latin America and Caribbean, Northern America, Europe, Oceania, Asia), income level (high income, upper-middle income, lower-middle income, and low income), and WHO region (Africa region (AFRO), Americas region (PAHO), East Mediterranean region (EMRO), Europe region (EURO), South-East Asia region (SEARO), and Western Pacific region (WPRO)).

Estimate of age-specific incidence rate
Different methods are employed to estimate age-specific incidence rates of cancer in GLOBO-CAN 2020, which can vary across countries.The accuracy of these estimates relies on the quality and availability of information.In the 184 countries under study, the methods used to estimate sex-and age-specific incidence rates are categorized in order of priority as follows: 1.National rates (or sub-national rates with coverage exceeding 50%) projected to 2020.2(a).Most recent rates from a single registry applied to the 2020 population.2(b).Weighted or simple average of the most recent sub-national rates applied to the 2020 population.3(a).Estimated from national mortality estimates using modeling, incorporating mortality-toincidence ratios derived from country-specific cancer registry data.3(b).Estimated from national mortality estimates using modeling, incorporating mortality-to-incidence ratios derived from cancer registry data in neighboring countries.3(c).Estimated from national mortality estimates using modeling, incorporating mortality-to-incidence ratios derived from survival estimation.4. "All sites" estimates obtained from neighboring countries, partitioned using frequency data. 5.When no data is available, rates are based on neighboring countries or registries in the same area [21].

Estimate of age-specific mortality rate
For age-specific mortality rates of cancer in GLOBOCAN 2020, the estimation methods depend on the quality and accuracy of national mortality data.The prioritized methods are as follows: 1.National rates projected to 2020.2(a).Most recent rates from a single source applied to the 2020 population.2(b).Weighted or simple average of the most recent local rates applied to the 2020 population.3(a).Estimated from national mortality estimates using modeling, incorporating mortality-to-incidence ratios derived from country-specific cancer registry data.3(b).Estimated from national mortality estimates using modeling, incorporating mortality-toincidence ratios derived from cancer registry data in neighboring countries.3(c).Estimated from national mortality estimates using modeling, incorporating mortality-to-incidence ratios derived from survival estimation.4. When no data is available, rates are based on neighboring countries in the same area [21].

Estimate of age-standardized incidence and mortality rates
An age-standardized rate (ASR) is a helpful measure that takes into account the impact of age when comparing rates among populations with different age structures.To calculate the ASR, the age-specific rates are weighted based on the distribution of a standard population.The world standard population is commonly used for this purpose.The resulting age-standardized incidence or mortality rate is expressed per 100,000 person-years.The world standard population used in GLOBOCAN was originally proposed by Segi in 1960 and later modified by Doll et al. in 1966 [21].

Human Development Index (HDI)
The Human Development Index (HDI) is a metric introduced by the United Nations Development Programme (UNDP) in 1990.It is a composite index that measures human development based on three dimensions: life expectancy at birth, mean years of schooling, and gross national income (GNI) per capita.To calculate the HDI, several key indicators related to health, education, and income are considered.These indicators provide a holistic view of a country's level of human development.The components taken into account include life expectancy at birth, which reflects the average number of years a person is expected to live, education indicators such as mean years of schooling and expected years of schooling, and GNI per capita, which measures the average income earned by individuals in a country.Once these indicators are collected, they undergo normalization and are combined using a specific formula to calculate the HDI.The formula aggregates the indicators and yields a single value ranging from 0 to 1, with 1 representing the highest level of human development.The HDI serves as a comprehensive assessment of human well-being and is a valuable tool for comparing development levels across countries and regions [33].

Statistical analysis
In this study, we presented information on the incidence and mortality rates of CL in 2020, including both raw and age-standardized rates expressed per 100,000 individuals.Before analyzing the data, we examined the rates and corresponding numbers of incidence and mortality for each region and country.We also assessed the coordination between the data and the population of each country.In this research, we utilized the Pearson's correlation coefficient to examine the association between the HDI and its components, such as average life expectancy at birth, mean years of schooling, and GNI per capita, with the ASIR and ASMR of CL.The Pearson's correlation coefficient test evaluates linear relationship between two continuous variables, assuming prerequisites such as continuous variables, linearity, normality, homoscedasticity, and independence.The magnitude of the correlation coefficient (r) is categorized into three groups: 0 to 0.25 indicates a weak or no correlation, 0.25 to 0.75 suggests an intermediate correlation, and 0.75 to 1 represents a strong correlation [34].For the examination of the relationship between quantitative variables using Pearson's correlation coefficient test, we included information from 176 countries in the analysis.A significance level of P<0.05 was considered statistically significant, and all reported P-values are two-sided.We used SPSS software (Version 26.0, SPSS Inc.) for all statistical analyses.

Geographical distribution
The ASIR and ASMR of CL in the world.In 2020, there were a total of 67,008 new cases of CL reported worldwide.Among these cases, 57.85% (38,767) occurred in males, while 42.15% (28,241) occurred in females.The overall ASIR for CL was 3.4, with a rate of 3.9 in males and 3 in females.The sex ratio, which compares the number of newly diagnosed cases between males and females, was 1.37.
Furthermore, there were a total of 25,080 deaths attributed to CL in the same year.Among these deaths, 58.86% (14,687) occurred in males, while 41.44% (10,393) occurred in females.The overall ASMR for CL was 1.3, with a rate of 1.4 in males and 1.1 in females.The sex ratio of mortality from CL was 1.41.
The ASIR of CL varied across different regions.In North America, the ASIR was 5.4 (5.7 in males and 5 in females).In Europe, it was 5.2 (5.5 in males and 4.9 in females).Oceania had an ASIR of 4.6 (5.1 in males and 4 in females).Asia had an ASIR of 4 (4.5 in males and 3.4 in females).Latin America and the Caribbean had an ASIR of 4.9 (5.4 in males and 4.4 in females).Africa had the lowest ASIR at 1.4 (1.6 in males and 1.2 in females) (Table 1).More detailed information about CL incidence rates in countries worldwide can be found in Tables 1 and 3 in the S1 File.
The ASMR of CL also varied across different regions.In North America, the ASMR was 0.55 (0.60 in males and 0.50 in females).In Europe, it was 0.73 (0.77 in males and 0.68 in females).Asia had an ASMR of 1.6 (1.8 in males and 1.3 in females).Oceania had an ASMR of 0.78 (0.92 in males and 0.62 in females).Latin America and the Caribbean had an ASMR of 2.0 (2.3 in males and 1.7 in females).Africa had the lowest ASMR at 0.69 (0.79 in males and 0.59 in females) (Table 2).More detailed information about CL mortality rates in countries worldwide can be found in Tables 2 and 3 in the S1 File.

The ASIR and ASMR of CL according to the WHO classification
The ASIR of CL varied across different regions.In the WPRO region, the ASIR was 5 (5.4 in males and 4.6 in females).In the EURO region, it was 4.9 (5.3 in males and 4.4 in females).PAHO had an ASIR of 5.1 (5.5 in males and 4.6 in females).In the SEARO region, it was 3.4 (4 in males and 2.8 in females).EMRO had an ASIR of 3.1 (3.6 in males and 2.7 in females).The lowest ASIR was in the AFRO region at 1.2 (1.4 in males and 1.1 in females).
The highest proportion of CL incidence occurred in the WPRO region, accounting for 26.39% of cases.EURO had 11.89% of cases, while PAHO had 16.30%.SEARO had 25.78%, EMRO had 11.12%, and AFRO had 8.50% (Table 1).Similarly, the ASMR of CL varied across regions.In the WPRO region, the ASMR was 1.7 (1.9 in males and 1.6 in females).In EURO, it was 0.94 (1 in males and 0.87 in females).PAHO had an ASMR of 1.6 (1.8 in males and 1.3 in females).In the SEARO region, it was 1.5 (1.7 in males and 1.2 in females).EMRO had an ASMR of 1.3 (1.6 in males and 1.1 in females).The lowest ASMR was in the AFRO region at 0.63 (0.71 in males and 0.55 in females).
The highest proportion of CL mortality occurred in the SEARO region, accounting for 30.44% of deaths.WPRO had 24.77% of deaths, while PAHO had 13.90%, EURO had 6.29%, EMRO had 12.85%, and AFRO had 11.72% (Table 2).

The ASIR and ASMR of CL in the countries of the world
The study revealed that China, India, and Indonesia had the highest number of new CL cases, with 11,985, 11,850, and 3,282 cases respectively.On the other hand, Vanuatu, Bhutan, and Luxembourg had the lowest number of cases, each with only one case.
In terms of ASIR of CL, Singapore, Malaysia, and Costa Rica had the highest rates at 8.4, 8.1, and 8.0 per 100,000 respectively.On the contrary, Guinea, Liberia, and Guinea-Bissau had the lowest ASIR, with rates of 0.14, 0.24, and 0.37 respectively (Table 3 in S1 File).
Moving on to mortality, India, China, and Indonesia had the highest number of CL cases, with 5,441, 4,468, and 1,280 cases respectively.Conversely, Vanuatu, Cabo Verde, and Bahrain had the lowest number of cases, each with only one case.Additionally, French Polynesia, Honduras, and Guyana had the highest ASMR of CL, with rates of 3.9, 3.4, and 3.2 respectively.On the other hand, Guinea, Republic of Congo, and Liberia had the lowest ASMR, with rates of 0.07, 0.17, and 0.19 respectively (Table 2 in S1 File).

Relationship between ASIR and ASMR of CL
A statistically significant positive correlation was found between the ASIR and ASMR of CL worldwide, with a correlation coefficient of 0.405 (P < 0.001).

The ASIR and HDI
Our analysis revealed a statistically significant positive correlation between the ASIR of CL and the HDI, with a correlation coefficient of 0.770 (P = 0.001).Furthermore, we observed positive correlations between the ASIR and various dimensions of HDI.Specifically, there was a positive correlation coefficient of 0.784 (P = 0.001) between the ASIR and life expectancy at birth.Similarly, there was a positive correlation coefficient of 0.663 (P = 0.001) between the ASIR and mean years of schooling.Additionally, we found a positive correlation coefficient of 0.597 (P = 0.001) between the ASIR and the level of income per person in the population.Please refer to Table 3 and the Tables 3 and 4 in S1 File for more detailed information.

The ASMR and HDI
Our analysis did not find a significant correlation between the ASMR for CL and the HDI, with a correlation coefficient of 0.077 (P = 0.337).However, we did observe a positive correlation coefficient of 0.185 (P = 0.021) between the ASMR and life expectancy at birth.The correlation between the ASMR and mean years of schooling was not significant, with a correlation coefficient of 0.037 (P = 0.646).Interestingly, we found a significant negative correlation coefficient of -0.166 (P = 0.038) between the ASMR of the CL and the level of income per person in the population.For more detailed information, please refer to Table 3 and the Tables 3 and 4 in S1 File.

Discussion
The study analyzed data from the internationally conducted GLOBOCAN PROJECT by WHO in 2020.Its aim was to explore the global incidence and mortality rates of CL in 2020 and their relationship with the HDI.The findings of the study unveiled notable variations in the geographical distribution of ASIR and ASMR of CL worldwide.These disparities highlight the importance of targeted preventive and therapeutic interventions that are customized to specific regions based on their rates of CL incidence and mortality.It is important to note that a positive correlation exists between the ASIR of CL and the HDI.This suggests that regions with higher development tend to have higher rates of CL incidence.In a study conducted by Namayandeh et al. using data from the 2018 global cancer study, a statistically significant correlation was found between the HDI and the ASIR of leukemia in children aged 0 to 14 years [6].This finding aligns with previous studies, which suggest that improved diagnostic capabilities in more developed areas may lead to higher detection rates.Additionally, certain lifestyle factors or environmental exposures linked to development may play a role in higher incidence rates [35].
Nonetheless, the analysis revealed that the correlation between the ASMR for CL and HDI was found to be insignificant.Similarly, Namayandeh et al.'s study did not observe a significant association between HDI and the ASMR of leukemia [6].This could be attributed to factors such as effective healthcare systems in high-HDI countries, which may lead to improved prognosis for leukemia.Additionally, variations in the types and stages of leukemia detected across different regions could contribute to these findings [27].
Interestingly, a significant negative correlation was discovered between the ASMR of CL and the level of income per person in the population.This negative correlation can be attributed to several possible reasons.Firstly, lower-income populations may face barriers in accessing healthcare services, including early detection and timely treatment of leukemia.Limited resources and financial constraints can hinder individuals from seeking appropriate medical care, leading to higher mortality rates.Additionally, socioeconomic factors such as education and awareness can play a role [36,37].Lower-income communities may have limited access to health education and information about cancer prevention, symptoms, and available treatment options.This lack of knowledge can contribute to delayed diagnosis and treatment, resulting in higher mortality rates [3,4].Furthermore, lifestyle factors associated with lowerincome populations, such as higher rates of smoking, exposure to environmental toxins, and limited access to nutritious food, can also contribute to an increased risk of developing leukemia and poorer health outcomes [2,9,21,37].It is important to note that these are potential reasons, and further research is needed to thoroughly understand the complex relationship between income level and CL mortality rates [38].
These findings offer valuable insights into the incidence and mortality rates of leukemia in various countries, providing guidance for prevention and treatment efforts.Further research is required to enhance our understanding of the factors influencing these differences, enabling the development of more effective strategies for CL prevention and treatment.The results of this study underscore the importance of adopting a comprehensive, global approach to lessen the burden of CL.Such an approach should take into account regional disparities in incidence and mortality rates, the socio-economic context, and the local healthcare infrastructure.
Leukemia is a complex disease with various subtypes, including acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myeloid leukemia (CML), among others [34,39].These subtypes differ in terms of aggressiveness, treatment response, and prognosis.The incidence and mortality rates of these subtypes can impact the trends of ASIR and ASMR differently.Access to healthcare also plays a crucial role in these trends.Disparities in healthcare infrastructure, resources, and availability of specialized treatment centers can affect leukemia diagnosis, treatment, and management [40,41].Regions or communities with limited healthcare access may experience higher mortality rates due to delayed diagnosis, inadequate treatment, or limited access to supportive care services [42,43].Additionally, socioeconomic factors such as income level, education, and health insurance coverage can influence access to care [44].Lower socioeconomic status individuals may face barriers in accessing timely and appropriate medical interventions [45], resulting in higher mortality rates.Considering these factors is important when analyzing ASIR and ASMR trends in CL.Understanding the differences in leukemia subtypes and access to care can help identify areas for improvement in prevention, early detection, treatment, and supportive care services, ultimately reducing mortality rates and improving overall outcomes.
The study did not specifically investigate the reasons for the difference in the incidence and mortality rates of leukemia between male and female children.However, several factors could potentially contribute to these variations [41,46].Genetic and hormonal differences between males and females can influence the development and progression of leukemia.Certain genetic mutations or variations may be more prevalent in one gender, making them more susceptible to specific types of leukemia.Hormonal factors, such as the influence of sex hormones, could also play a role in these observed differences.Lifestyle and environmental factors may also contribute to the discrepancy.Additionally, differences in healthcare-seeking behaviors or access to healthcare services between males and females could impact early detection, diagnosis, and treatment, potentially leading to variations in the observed rates [36,46].
Moving forward, there are several key areas for future research on the topic of leukemia:1-In-depth analysis of regional disparities: Further investigation is needed to understand the underlying factors contributing to the variations in CL incidence and mortality rates across different regions.This could involve examining socioeconomic, environmental, and genetic factors that may influence the disease burden.2-Longitudinal studies: Conducting long-term studies can provide valuable insights into the trends and changes in CL incidence and mortality rates over time.This would help identify emerging patterns and potential risk factors that may contribute to the development of the disease.3-Comparativeanalysis of healthcare systems: Exploring the impact of different healthcare systems on CL outcomes can contribute to understanding the effectiveness of various healthcare approaches.Comparing regions with differing healthcare infrastructures and resources may provide insights into best practices and areas for improvement.4-Investigation of risk factors: Further research should be conducted to identify specific risk factors associated with the development of CL in different regions.This could involve studying environmental exposures, lifestyle factors, genetic predispositions, and occupational hazards to gain a better understanding of the etiology of the disease.5-Evaluation of intervention strategies: Future research should focus on evaluating the effectiveness of targeted interventions and strategies implemented to address regional disparities in CL.This could involve studying the impact of early detection programs, access to quality healthcare, and the implementation of preventive measures.By exploring these research directions, we can enhance our understanding of CL distribution and its associated factors.This knowledge can inform the development of evidence-based interventions and policies to improve CL outcomes globally.
The findings and conclusions of this study offer valuable insights to guide prevention and treatment efforts for CL.By understanding the relationship between factors such as the HDI and leukemia incidence, healthcare professionals and policymakers can develop targeted interventions and strategies to reduce the burden of this disease.For instance, identifying a significant relationship between the HDI and leukemia incidence in children highlights the importance of addressing social determinants of health.Efforts can be made to improve access to healthcare services, particularly in lower-income communities, ensuring early detection, prompt diagnosis, and timely treatment.Increasing awareness about CL and its risk factors can also empower parents, caregivers, and healthcare providers to recognize potential symptoms and seek medical attention promptly.

Limitations of the study
It is important to acknowledge that the quality of cancer data collected in the GLOBOCAN project may vary across countries, particularly those with medium or low HDI.This could result in estimates based on limited areas or neighboring country data for some countries [47].For more specific information, please refer to Tables 3 and 4 in S1 File.In addition, as mentioned earlier, ecological studies have inherent limitations that can impact their results.It is crucial to take these limitations into account when interpreting the findings of such studies.Some of the common limitations include: i. Ecological fallacy: Ecological studies often rely on average measures of exposure within populations, which may not accurately represent individual-level exposures.Applying these grouped results to individuals can lead to erroneous assumptions, known as the ecological fallacy.ii.Differences in disease recording: Systematic variations may exist in the recording of disease frequency among different areas.Factors such as disease coding, classification, diagnosis, and reporting completeness can differ across regions or countries, introducing potential bias into the study outcomes.iii.Differences in exposure measurement: The measurement of exposures may also vary between different areas in ecological studies.This can introduce discrepancies in exposure assessment, potentially affecting the accuracy and reliability of the study's findings.iv.Lack of available data on confounding factors: Ecological studies may face challenges in obtaining comprehensive data on confounding factors -other variables that can influence the relationship between exposure and outcome.The absence of such data can limit the ability to account for potential confounding effects and draw accurate conclusions.v. sparsity of data for some countries: Some countries had sparse data, which means that there was limited or incomplete information available for those specific regions.It is crucial to be aware of these limitations when interpreting the results of ecological studies and to understand their implications for the validity and generalizability of the findings.

Conclusion
The study revealed significant variations in CL incidence and mortality rates among different regions.North America and Europe had higher rates, while Africa had the lowest rates.Furthermore, a positive correlation was observed between the ASIR and the HDI, while no significant correlation was found between the ASMR and HDI.Understanding the geographical distribution of CL can inform the development of targeted interventions and strategies to address regional disparities and improve patient outcomes.

Table 3 . The relationship between the incidence and mortality of leukemia with the HDI and its components. Incidence and mortality rates HDI Life expectancy at birth Mean years of schooling Gross national Income (GNI) per capita
https://doi.org/10.1371/journal.pone.0304354.t003